title: Identifying proteins controlling key disease signaling pathways.
abstract: BACKGROUND
  Several types of studies, including genome-wide association studies and RNA interference screens, strive to link genes to diseases. Although these approaches have had some success, genetic variants are often only present in a small subset of the population, and screens are noisy with low overlap between experiments in different labs. Neither provides a mechanistic model explaining how identified genes impact the disease of interest or the dynamics of the pathways those genes regulate. Such mechanistic models could be used to accurately predict downstream effects of knocking down pathway members and allow comprehensive exploration of the effects of targeting pairs or higher-order combinations of genes.
  
  
  RESULTS
  We developed methods to model the activation of signaling and dynamic regulatory networks involved in disease progression. Our model, SDREM, integrates static and time series data to link proteins and the pathways they regulate in these networks. SDREM uses prior information about proteins' likelihood of involvement in a disease (e.g. from screens) to improve the quality of the predicted signaling pathways. We used our algorithms to study the human immune response to H1N1 influenza infection. The resulting networks correctly identified many of the known pathways and transcriptional regulators of this disease. Furthermore, they accurately predict RNA interference effects and can be used to infer genetic interactions, greatly improving over other methods suggested for this task. Applying our method to the more pathogenic H5N1 influenza allowed us to identify several strain-specific targets of this infection.
  
  
  BACKGROUND
  SDREM is available from http://sb.cs.cmu.edu/sdrem.
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Computer Science Department and Lane Center for Computational Biology, School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA 15213, USA.
authors.forename: Anthony
authors.initials: A
authors.lastname: Gitter
authors.forename: Ziv
authors.initials: Z
authors.lastname: Bar-Joseph
mesh_terms: D000465:Algorithms; D018450:Disease Progression; D020869:Gene Expression Profiling; D053263:Gene Regulatory Networks; D055106:Genome-Wide Association Study; D006801:Humans; D053118:Influenza A Virus, H1N1 Subtype; D053124:Influenza A Virus, H5N1 Subtype; D007251:Influenza, Human / Q000235:genetics* / Q000378:metabolism*; D025941:Protein Interaction Mapping; D034622:RNA Interference; D015398:Signal Transduction; D014157:Transcription Factors / Q000378:metabolism; D019562:Viral Load
chemical_list: D014157:Transcription Factors